笔者使用下面的代码llama2_train.py进行模型微调: importtorchfromtransformersimportAutoTokenizer,AutoModelForCausalLM,BitsAndBytesConfig,TrainingArgumentsfromdatasetsimportload_datasetfromtrlimportSFTTrainerfrompeftimportLoraConfigdataset=load_dataset("wikitext","wikitext-2-raw-v1",split="train")# Hugging Face...
we use huggingfaceshibin6624/medicalto fine-tuning llama2, please note that this dataset is consist of en and cn data, here we just use en data. dataset structure now we download and load dataset, then save them into train.csv, validation.csv and test.csv. fromdatasetsimportload_datasetimpo...
Fine-tuning larger LLMs, such as the Llama 2 70B, demands increased computational power, VRAM, and time. In our assessments with configurations of 4 and 8 Intel® Data Center GPU Max Series cards on a single server, we observed notable efficiency gains. Specifically, a single ...
这里选择GuanacoDataset数据集。数据格式如下: 可以看到里面包含了各类任务,而且还有其它语言。 6.4 微调模型 数据准备好后,接下来就可以微调模型。如果是单卡,可以使用如下脚本: #if running on multi-gpu machine export CUDA_VISIBLE_DEVICES=0 python llama_finetuning.py --use_peft --peft_method lora --qu...
def convert_dataset(ds): prompts = [format_prompt(i, choice_prefixes) for i in ds if i['hint'] != ''] labels = [format_response(i, choice_prefixes) for i in ds if i['hint'] != ''] df = pd.DataFrame.from_dict({'prompt': prompts, 'response': labels}) return ...
from datasets import load_dataset from peft import ( LoraConfig, PeftModel, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, prepare_model_for_kbit_training, set_peft_model_state_dict, ) import transformers ...
dataset = "dataset_name" peft_method: str = "lora" output_dir: str = "path_to_save_fine_tuning_model"save_model: bool = True 6.Run the following command to perform fine tuning on a single GPU python3 llama_finetuning.py --use_peft --peft_method lora --quantization --model_...
The final task we consider is GSM8k. This task is a standard academic benchmark for evaluating LLMs on math reasoning and understanding. The challenge of fine-tuning on this dataset differs from the previous two. As opposed to just learning structure, we wanted to see how much ...
print(dataset[randrange(len(dataset))]) 创建提示 为了执行指令微调,我们必须将每个数据示例转换为指令,并将其主要部分概述如下: def format_instruction(sample): return f"""### Instruction: Use the Task below and the Input given to write the Response, which is a programming code that can solve th...
1python src/train_bash.py \2--stage sft \3--model_name_or_path /hy-tmp/tigerbot-70b-chat-v4-4k \4--do_train True \5--finetuning_type freeze \6--num_layer_trainable2\7--template tigerbot \8--dataset_dir data \9--dataset self_cognition_golden \10--cutoff_len4096\11--learning...